1. Field of Art
The disclosure generally relates to the field of transportation system solutions, and specifically to reconstruction of the transportation system.
2. Description of the Related Art
Transportation systems provide transport service to streams of commuters who arrive at train stations, bus stops or highway entrances at random times. The “transportation service” a commuter wants is a trip that starts at one node (station, stop or highway entrance) of the transportation systems and ends at another node of the systems. During the course of obtaining this service, the commuter may make a mix of train, bus, car and/or bike journeys. The “commuting history” of a commuter, i.e., the trips made by that commuter over time (weeks, months), can possess discernible patterns (e.g., a particular commuter traverses the same route to work or school from home each day and vice versa) or be arbitrary (e.g., tourists visiting different parts of the city).
The collection of all trips made by the users of a transportation system in a day, duly annotated with metadata such as the origin and destinations of the trip, the swipe-in (entry) and swipe-out (exit) times, etc., makes up “the daily trip records” or “the daily fare records”The daily trip records represent the daily transportation demand placed upon the transportation system. In addition, the collection of all trips made by the vehicles in the transportation fleet including public transportation as well as private vehicles and other private systems, annotated with metadata such as the route, the exact location of each vehicle at any time, or the times of arrival and departure at each station or stop on the route, makes up of “the fleet trip details” and represents the available daily transport supply. When the daily demand is presented to the daily supply in a transport network, each commuter obtains a “service” from the network.
However, current transportation analysis systems do not provide a complete picture of the transportation systems. Some solutions may use schedules or Global Positioning System (GPS) information on vehicles to provide information about system operations, and provide the ability to send alerts to emergency responders. However, these methods do not provide visibility into how many people are using the transportation systems, and in what ways people are using the systems. Neither do these methods provide visibility into the quality of service provided by the transportations systems to the people. Furthermore, the current transportation solutions are limited in their abilities to perform sensitivity analyses, to run robust reports quickly and easily, and to collaborate on and share information.
In addition, while trip records are sometimes available, they also provide an incomplete picture of a system and the people who use the system. Each record might indicate an individual journey (e.g., a starting and ending point of a journey); however the finer details such as the actual train a commuter boards, the waiting time before boarding the train, etc., might not be available. As a result, it is difficult to see trends over certain time periods and/or stations.
Understanding the transportation system in aggregate is also a challenge. To know whether or not a system is performing well and to optimize the operations of the system, transportation system operators need to know quantities like occupancy, departures, waiting times at stations or platforms, etc., over time and on particular routes or at certain stops or stations. Current attempted solutions are expensive, incomplete, inaccurate, or a combination thereof. For example, certain current trains have sensors that can calculate the weight and then estimate a rough number of people on a train. Other current systems add GPS units or Radio Frequency Identification (RFID) scans to get information about a vehicle location. Other operation systems employ people to administer surveys and/or count the number of people in each train or on the platform at selected points in time. Therefore, all above current solutions involve additional hardware, instrumentation, or people.
Current transportation solutions, whether such approaches are technological, human, or otherwise, do not provide a complete picture of transportation systems. As a result, operators and planners lack the information to run their systems more efficiently. In addition, no current solutions infer system and traveler behavior from disparate sources of data, and use these inferences to supply a comprehensive transportation management solution. Accordingly, there is a need for a suite of methods for inferring, from incomplete or coarse-grained records, the fine details of the operation of a transit system.